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Ph.D de

Ph.D
Group : Large-scale Heterogeneous DAta and Knowledge

Adaptive Methods for User-Centric Information Access Applications

Starts on 01/10/2014
Advisor : CAUTIS, Bogdan

Funding : Contrat doctoral uniquement recherche
Affiliation : Université Paris-Sud
Laboratory : LRI - LaHDAK

Defended on 12/10/2017, committee :
Rapporteurs:
Ludovic Denoyer, UPMC Université Paris 6 (France)
Pierre Senellart, Ecole Normale Supérieure (France)

Examinateurs:
Aurélien Garivier, Université Paul Sabatier (France)
Stratis Ioannidis, Northeastern University (Etats-Unis)
Themis Palpanas, Université Paris Descartes (France)
Fabian Suchanek, Télécom ParisTech (France)

Directeurs de thèse:
Olivier Cappé, LIMSI (France)
Bogdan Cautis, Université Paris-Sud (France)

Research activities :

Abstract :
When users interact on modern Web systems, they let numerous footprints which we
propose to exploit in order to develop better applications for information
access. We study a family of techniques centered on users, which take advantage
of the many types of feedback to adapt and improve services provided to users.
The first part of this thesis is dedicated to an approach for as-you-type search
on social media. The problem consists in retrieving a set of k search results in
a social-aware environment under the constraint that the query may be incomplete
(e.g., if the last term is a prefix). We adopt a "network-aware" interpretation
of information relevance, by which information produced by users who are closer
to the user issuing a request is considered more relevant. Then, we study a
generic version of influence maximization, in which we want to maximize the
influence of marketing or information campaigns by adaptively selecting "spread
seeds" from a small subset of the population. Finally, we propose to address the
well-known cold start problem faced by recommender systems with an adaptive
approach. We introduce a bandit algorithm that aims to intelligently achieve the
balance between "bad" and "good" recommendations.

Ph.D. dissertations & Faculty habilitations
DECODING THE PLATFORM SOCIETY: ORGANIZATIONS, MARKETS AND NETWORKS IN THE DIGITAL ECONOMY
The original manuscript conceptualizes the recent rise of digital platforms along three main dimensions: their nature of coordination devices fueled by data, the ensuing transformations of labor, and the accompanying promises of societal innovation. The overall ambition is to unpack the coordination role of the platform and where it stands in the horizon of the classical firm – market duality. It is also to precisely understand how it uses data to do so, where it drives labor, and how it accommodates socially innovative projects. I extend this analysis to show continuity between today’s society dominated by platforms and the “organizational society”, claiming that platforms are organized structures that distribute resources, produce asymmetries of wealth and power, and push social innovation to the periphery of the system. I discuss the policy implications of these tendencies and propose avenues for follow-up research.

DISTRIBUTED COMPUTING WITH LIMITED RESOURCES


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